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arXiv 1304.3071math.OCcs.SYeess.SY
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最小子集可控性问题

Minimal Controllability Problems

University of Illinois at Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校)

Alex Olshevsky

AI总结:

本文研究了线性系统中通过最小变量集实现可控性的难题,证明该问题NP难,并提出贪心算法在多项式时间内解决,实验显示其在随机图中有效。

摘要:

给定一个线性系统,我们考虑寻找一个小型变量集以输入影响,使得系统可控的问题。我们证明该问题NP难;确实,我们证明即使近似最小需要影响的变量数在乘法因子c log n内也是NP难的,对于某些正数c。积极的一面是,我们展示可以以多项式时间找到匹配此不可近似障碍的变量集。这可以通过一个简单的贪心启发式方法实现,该方法依次选择以最大化可控性矩阵的秩增加。在Erdos-Renyi随机图上的实验显示,该启发式方法几乎总能成功找到最小变量数。

英文摘要:

Given a linear system, we consider the problem of finding a small set of variables to affect with an input so that the resulting system is controllable. We show that this problem is NP-hard; indeed, we show that even approximating the minimum number of variables that need to be affected within a multiplicative factor of $c \log n$ is NP-hard for some positive $c$. On the positive side, we show it is possible to find sets of variables matching this inapproximability barrier in polynomial time. This can be done by a simple greedy heuristic which sequentially picks variables to maximize the rank increase of the controllability matrix. Experiments on Erdos-Renyi random graphs demonstrate this heuristic almost always succeeds at findings the minimum number of variables.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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